Cell-based screening is a technology used for exploring normal cellular processes and modulation thereto caused by chemical, infection, and/or genetic changes. Screening has conventionally been a challenge as multiple aspects of experimental biology are performed, including, for example, the preparation of cells, automated microscopy, high throughput screening of large compound libraries, and the development of image analysis and pattern recognition linked to high level bioinformatics databases. These considerations have motivated the development of high content screening methods, which are based on the microscopy of modified host cells to show the activity or organization of molecular targets inside the living cells. Primarily, these techniques have been used for identifying drug candidates for a particular disease or exploring a functional aspect of a given subcellular molecule, including genes, effects of toxins, and material and environmental conditions using statistical analysis.
For example, FIGS. 1A and 1B illustrate monocyte-derived macrophages 10, 12 in a healthy condition (FIG. 1A) and an infected condition (FIG. 1B). Macrophages, along with monocytes, from which macrophages are produced, function in non-specific defense. Accordingly, macrophages are mobile, such as by amoeboid movements, by extending pseudopodia 14, 16 so as to engulf likely pathogens, for example, bacterium, by phagocytosis. The macrophage includes a cell membrane 18, 20, a nucleus 22, 24, and a plurality of organelles and cytoskeletal features (not shown) within the cytoplasm 26, 28. Phenotypic changes to the macrophage due to bacterial infection may include: cell size (the infected macrophage 12 is shown to be larger than the normal macrophage 10), cell shape (the infected macrophage 12 is shown to have more pronounced pseudopods 16 than the normal macrophage 10), multi-scale features, invariant moment features, statistical texture features at different scales, Laws texture features, differential features of the intensity surface (features of local gradient magnitude, local gradient orientation, Laplacian, isophote, flowline, brightness, shape index, etc.), frequency domain features, histogram features distribution features (radial, angular, etc., of intensity distribution, gradient magnitude distribution), local binary pattern image features, local contrast pattern image features, cell boundary features, edge features and other heuristic features, such as spottiness, Chi-square distance between histograms of pixel patches, between concentric circular areas within the cell, gray-class distance, and heuristic and problem specific features, to name a few.
Cellular features may be examined by way of microscope or microscopic imaging. For example, bright field microscopy images provide a quick and efficient method of detecting the cells and cellular features. Cellular detection, counting, and classification on bright field images may automated; however, images of cell populations tend to be low contrast images with uneven illumination, may contain objects with uneven textures, and may include debris and other artifacts, which makes automation difficult.
As a result, there remains a need for techniques that may, at least partially, automate bright field microscopy imaging, pre- and post processing of images, and screening of cell populations for investigating phenotypic changes due to chemicals, toxins, infection, genetic alteration, or environmental conditions.